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        <span>不同特征数之间的分类模型比较</span>
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        <h1 id="不同特征数之间的分类模型比较"><a href="#不同特征数之间的分类模型比较" class="headerlink" title="不同特征数之间的分类模型比较"></a>不同特征数之间的分类模型比较</h1><p>首先确定特征的个数，确定index和特征数之间的关系</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"></span><br><span class="line"><span class="comment"># 选定固定的特征值</span></span><br><span class="line">feature_import = pd.read_excel(<span class="string">&#x27;D:/zccode&#x27;</span> + <span class="string">&#x27;/feature_important&#x27;</span> + <span class="string">&#x27;%s&#x27;</span> % <span class="number">2</span> + <span class="string">&#x27;.xlsx&#x27;</span>)</span><br><span class="line">df = pd.get_dummies(feature_import.iloc[<span class="number">0</span>:<span class="built_in">len</span>(feature_import), <span class="number">1</span>:<span class="number">5</span>])</span><br></pre></td></tr></table></figure>

<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line">      <span class="number">5</span>NN20    <span class="number">5</span>pNN20    <span class="number">5</span>pNN50     <span class="number">5</span>NN50</span><br><span class="line"><span class="number">0</span>  <span class="number">0.023794</span>  <span class="number">0.023346</span>  <span class="number">0.022757</span>  <span class="number">0.022194</span></span><br></pre></td></tr></table></figure>

<p>根据设定的index,还是取的是左闭右开，因此对于下面的特征，index 应该为特征数加1</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br></pre></td><td class="code"><pre><span class="line">最大准确率时对应的特征数</span><br><span class="line">分期为<span class="number">5</span>时	最高准确率为<span class="number">79.65</span>%	特征数为<span class="number">23</span></span><br><span class="line">分期为<span class="number">4</span>时	最高准确率为<span class="number">86.40</span>%	特征数为<span class="number">23</span></span><br><span class="line">分期为<span class="number">3</span>时	最高准确率为<span class="number">89.04</span>%	特征数为<span class="number">15</span></span><br><span class="line">准确率降低<span class="number">1</span>%后对应的特征数</span><br><span class="line">分期为<span class="number">5</span>时	最高准确率为<span class="number">79.65</span>%	最高准确率降低<span class="number">1</span>%后为<span class="number">78.85</span>%	比较筛选的准确率为<span class="number">78.86</span>%	特征数为<span class="number">9</span></span><br><span class="line">分期为<span class="number">4</span>时	最高准确率为<span class="number">86.40</span>%	最高准确率降低<span class="number">1</span>%后为<span class="number">85.54</span>%	比较筛选的准确率为<span class="number">85.66</span>%	特征数为<span class="number">8</span></span><br><span class="line">分期为<span class="number">3</span>时	最高准确率为<span class="number">89.04</span>%	最高准确率降低<span class="number">1</span>%后为<span class="number">88.15</span>%	比较筛选的准确率为<span class="number">88.22</span>%	特征数为<span class="number">7</span></span><br></pre></td></tr></table></figure>

<p>重新跑下结果；</p>
<p>因此对于最大准确率时，基于特征数的分类模型就该为下面程序</p>
<h2 id="最大准确率时特征对应的分类结果"><a href="#最大准确率时特征对应的分类结果" class="headerlink" title="最大准确率时特征对应的分类结果"></a>最大准确率时特征对应的分类结果</h2><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># -*- coding: utf-8 -*-</span></span><br><span class="line"><span class="comment"># @time     : 2020/6/23 0023</span></span><br><span class="line"><span class="comment"># @Author   : esy</span></span><br><span class="line"></span><br><span class="line"><span class="keyword">from</span> sleep_class <span class="keyword">import</span> *</span><br><span class="line"><span class="keyword">for</span> k <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">3</span>):</span><br><span class="line">    <span class="keyword">if</span> k == <span class="number">0</span>:</span><br><span class="line">        index = <span class="number">24</span></span><br><span class="line">        class_feature(k, index)</span><br><span class="line">    <span class="keyword">elif</span> k == <span class="number">1</span>:</span><br><span class="line">        index = <span class="number">24</span></span><br><span class="line">        class_feature(k, index)</span><br><span class="line">    <span class="keyword">else</span>:</span><br><span class="line">        index = <span class="number">16</span></span><br><span class="line">        class_feature(k, index)</span><br></pre></td></tr></table></figure>

<h3 id="sleep-class-中的函数"><a href="#sleep-class-中的函数" class="headerlink" title="sleep class 中的函数"></a>sleep class 中的函数</h3><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># -*- coding: utf-8 -*-</span></span><br><span class="line"><span class="comment"># @time     : 2020/6/23 0023</span></span><br><span class="line"><span class="comment"># @Author   : esy</span></span><br><span class="line"></span><br><span class="line"><span class="keyword">from</span> data_preprocessing <span class="keyword">import</span> *</span><br><span class="line"><span class="keyword">from</span> sklearn.model_selection <span class="keyword">import</span> train_test_split</span><br><span class="line"><span class="keyword">from</span> classifiers <span class="keyword">import</span> *</span><br><span class="line"><span class="keyword">import</span> warnings</span><br><span class="line"></span><br><span class="line">warnings.filterwarnings(<span class="string">&quot;ignore&quot;</span>)</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">class_feature</span>(<span class="params">k, index</span>):</span></span><br><span class="line">    class_scores = []</span><br><span class="line">    kappa_scores = []</span><br><span class="line">    <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">1</span>, <span class="number">19</span>):</span><br><span class="line">        feature = pd.read_excel(<span class="string">&#x27;D:/zccode/all_feature&#x27;</span> + <span class="string">&#x27;/features&#x27;</span> + <span class="string">&#x27;%s&#x27;</span> % i + <span class="string">&#x27;.xlsx&#x27;</span>)</span><br><span class="line">        data = pd.get_dummies(feature.iloc[<span class="number">0</span>:<span class="built_in">len</span>(feature), <span class="number">1</span>:])</span><br><span class="line">        note = pd.read_excel(<span class="string">&#x27;D:/zccode/all_note&#x27;</span> + <span class="string">&#x27;/note&#x27;</span> + <span class="string">&#x27;%s&#x27;</span> % i + <span class="string">&#x27;.xlsx&#x27;</span>)</span><br><span class="line">        tag = pd.get_dummies(note.iloc[<span class="number">0</span>:<span class="built_in">len</span>(data), <span class="number">1</span>:])</span><br><span class="line">        feature_import = pd.read_excel(<span class="string">&#x27;D:/zccode&#x27;</span> + <span class="string">&#x27;/feature_important&#x27;</span> + <span class="string">&#x27;%s&#x27;</span> % (k+<span class="number">1</span>) + <span class="string">&#x27;.xlsx&#x27;</span>)</span><br><span class="line">        df = pd.get_dummies(feature_import.iloc[<span class="number">0</span>:<span class="built_in">len</span>(feature_import), <span class="number">1</span>:index])</span><br><span class="line"></span><br><span class="line">        std_data = data_pre(data[df.keys()])</span><br><span class="line">        label = pd.get_dummies(tag.iloc[<span class="number">0</span>:<span class="built_in">len</span>(data), k:k+<span class="number">1</span>])</span><br><span class="line"></span><br><span class="line">        class_score = []</span><br><span class="line">        kappa_score = []</span><br><span class="line">        <span class="keyword">for</span> x <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">50</span>):</span><br><span class="line">            X_train, X_test, y_train, y_test = train_test_split(std_data, label, test_size=<span class="number">0.3</span>)</span><br><span class="line">            score = run_classifiers(X_train, X_test, y_train, y_test)</span><br><span class="line">            class_score.append(score[<span class="number">0</span>])</span><br><span class="line">            kappa_score.append(score[<span class="number">1</span>])</span><br><span class="line">        class_scores.append([(np.array([class_score[a][k] <span class="keyword">for</span> a <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">50</span>)])).mean() <span class="keyword">for</span> k <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">9</span>)])</span><br><span class="line">        kappa_scores.append([(np.array([kappa_score[a][k] <span class="keyword">for</span> a <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">50</span>)])).mean() <span class="keyword">for</span> k <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">9</span>)])</span><br><span class="line">        print(<span class="string">f&#x27;已经运行<span class="subst">&#123;i&#125;</span>次&#x27;</span>)</span><br><span class="line"></span><br><span class="line">    class_total = pd.DataFrame(class_scores)</span><br><span class="line">    kappa_total = pd.DataFrame(kappa_scores)</span><br><span class="line"></span><br><span class="line">    class_total.to_excel(<span class="string">&#x27;feature_section_class&#x27;</span> + <span class="string">&#x27;%d&#x27;</span> % k + <span class="string">&quot;.xlsx&quot;</span>)</span><br><span class="line">    kappa_total.to_excel(<span class="string">&#x27;feature_section_kappa&#x27;</span> + <span class="string">&#x27;%d&#x27;</span> % k + <span class="string">&quot;.xlsx&quot;</span>)</span><br><span class="line">    <span class="keyword">return</span> <span class="string">f&#x27;运行一次&#x27;</span></span><br><span class="line"></span><br></pre></td></tr></table></figure>

<h2 id="特征降维后的分类结果"><a href="#特征降维后的分类结果" class="headerlink" title="特征降维后的分类结果"></a>特征降维后的分类结果</h2><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># -*- coding: utf-8 -*-</span></span><br><span class="line"><span class="comment"># @time     : 2020/6/23 0023</span></span><br><span class="line"><span class="comment"># @Author   : esy</span></span><br><span class="line"></span><br><span class="line"><span class="keyword">from</span> sleep_class <span class="keyword">import</span> *</span><br><span class="line"><span class="keyword">for</span> k <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">3</span>):</span><br><span class="line">    <span class="keyword">if</span> k == <span class="number">0</span>:</span><br><span class="line">        index = <span class="number">10</span></span><br><span class="line">        class_feature(k, index)</span><br><span class="line">    <span class="keyword">elif</span> k == <span class="number">1</span>:</span><br><span class="line">        index = <span class="number">9</span></span><br><span class="line">        class_feature(k, index)</span><br><span class="line">    <span class="keyword">else</span>:</span><br><span class="line">        index = <span class="number">8</span></span><br><span class="line">        class_feature(k, index)</span><br></pre></td></tr></table></figure>

<p>保存名字为 <strong>features_section_class</strong></p>
<p>然后将其取平均值，并绘制柱状图</p>
<h2 id="分类结果"><a href="#分类结果" class="headerlink" title="分类结果"></a>分类结果</h2><p>文件保存为：</p>
<p>E:\feature section</p>
<p> <strong>features_section_class</strong>是降维后</p>
<p> <strong>feature_section_class</strong> 是最开始的数据分类结果</p>
<p>经过查看，在即使在最高分期准确率的结果下，都会出现数据一部分特别好一部分都很差的情况</p>
<p>暂时只进行数据的平均分类准确率，然后筛选出最优的分类模型，比较每个模型中最优的结果</p>
<p>然后再去考虑单独的人，为什么会差距这么大，是什么因素影响了分期准确率</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br></pre></td><td class="code"><pre><span class="line">           <span class="number">0</span>         <span class="number">1</span>         <span class="number">2</span>  ...         <span class="number">6</span>         <span class="number">7</span>         <span class="number">8</span></span><br><span class="line"><span class="number">0</span>   <span class="number">0.785217</span>  <span class="number">0.862609</span>  <span class="number">0.828406</span>  ...  <span class="number">0.874493</span>  <span class="number">0.879130</span>  <span class="number">0.882029</span></span><br><span class="line"><span class="number">1</span>   <span class="number">0.789143</span>  <span class="number">0.824000</span>  <span class="number">0.843429</span>  ...  <span class="number">0.844952</span>  <span class="number">0.845714</span>  <span class="number">0.847810</span></span><br><span class="line"><span class="number">2</span>   <span class="number">0.834667</span>  <span class="number">0.866286</span>  <span class="number">0.857714</span>  ...  <span class="number">0.848381</span>  <span class="number">0.853524</span>  <span class="number">0.860381</span></span><br><span class="line"><span class="number">3</span>   <span class="number">0.881538</span>  <span class="number">0.890000</span>  <span class="number">0.886154</span>  ...  <span class="number">0.881795</span>  <span class="number">0.864615</span>  <span class="number">0.898718</span></span><br><span class="line"><span class="number">4</span>   <span class="number">0.601373</span>  <span class="number">0.694902</span>  <span class="number">0.687745</span>  ...  <span class="number">0.756569</span>  <span class="number">0.765588</span>  <span class="number">0.768333</span></span><br><span class="line"><span class="number">5</span>   <span class="number">0.754366</span>  <span class="number">0.805540</span>  <span class="number">0.804131</span>  ...  <span class="number">0.850704</span>  <span class="number">0.847512</span>  <span class="number">0.827793</span></span><br><span class="line"><span class="number">6</span>   <span class="number">0.615450</span>  <span class="number">0.674597</span>  <span class="number">0.664171</span>  ...  <span class="number">0.706161</span>  <span class="number">0.702749</span>  <span class="number">0.715735</span></span><br><span class="line"><span class="number">7</span>   <span class="number">0.754732</span>  <span class="number">0.831122</span>  <span class="number">0.803415</span>  ...  <span class="number">0.840293</span>  <span class="number">0.824195</span>  <span class="number">0.845951</span></span><br><span class="line"><span class="number">8</span>   <span class="number">0.811746</span>  <span class="number">0.826984</span>  <span class="number">0.831429</span>  ...  <span class="number">0.822751</span>  <span class="number">0.834921</span>  <span class="number">0.844339</span></span><br><span class="line"><span class="number">9</span>   <span class="number">0.915749</span>  <span class="number">0.928309</span>  <span class="number">0.927246</span>  ...  <span class="number">0.941739</span>  <span class="number">0.935556</span>  <span class="number">0.938744</span></span><br><span class="line"><span class="number">10</span>  <span class="number">0.535238</span>  <span class="number">0.640173</span>  <span class="number">0.591602</span>  ...  <span class="number">0.674026</span>  <span class="number">0.670216</span>  <span class="number">0.669524</span></span><br><span class="line"><span class="number">11</span>  <span class="number">0.695733</span>  <span class="number">0.780800</span>  <span class="number">0.759822</span>  ...  <span class="number">0.809689</span>  <span class="number">0.798489</span>  <span class="number">0.795289</span></span><br><span class="line"><span class="number">12</span>  <span class="number">0.640356</span>  <span class="number">0.700889</span>  <span class="number">0.687911</span>  ...  <span class="number">0.690844</span>  <span class="number">0.696978</span>  <span class="number">0.705422</span></span><br><span class="line"><span class="number">13</span>  <span class="number">0.694667</span>  <span class="number">0.804148</span>  <span class="number">0.751852</span>  ...  <span class="number">0.786519</span>  <span class="number">0.801037</span>  <span class="number">0.811556</span></span><br><span class="line"><span class="number">14</span>  <span class="number">0.803824</span>  <span class="number">0.833137</span>  <span class="number">0.834608</span>  ...  <span class="number">0.841373</span>  <span class="number">0.833529</span>  <span class="number">0.850392</span></span><br><span class="line"><span class="number">15</span>  <span class="number">0.646479</span>  <span class="number">0.679531</span>  <span class="number">0.723192</span>  ...  <span class="number">0.772582</span>  <span class="number">0.776901</span>  <span class="number">0.803474</span></span><br><span class="line"><span class="number">16</span>  <span class="number">0.682636</span>  <span class="number">0.745271</span>  <span class="number">0.743101</span>  ...  <span class="number">0.760930</span>  <span class="number">0.770078</span>  <span class="number">0.758140</span></span><br><span class="line"><span class="number">17</span>  <span class="number">0.668372</span>  <span class="number">0.708837</span>  <span class="number">0.700465</span>  ...  <span class="number">0.719535</span>  <span class="number">0.696744</span>  <span class="number">0.721395</span></span><br></pre></td></tr></table></figure>

<p>如下表所示，有些列的数据对应起来就并不好，先基于平均值然后再去筛选为什么为地域80%的原因，找单独的数据序列，去对应查看其中的影响因素。</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br></pre></td><td class="code"><pre><span class="line"> <span class="number">0</span>         <span class="number">1</span>         <span class="number">2</span>  ...         <span class="number">6</span>         <span class="number">7</span>         <span class="number">8</span></span><br><span class="line"><span class="number">0</span>   <span class="number">0.721449</span>  <span class="number">0.851304</span>  <span class="number">0.764348</span>  ...  <span class="number">0.848116</span>  <span class="number">0.836232</span>  <span class="number">0.851594</span></span><br><span class="line"><span class="number">1</span>   <span class="number">0.712571</span>  <span class="number">0.770095</span>  <span class="number">0.768381</span>  ...  <span class="number">0.808571</span>  <span class="number">0.814476</span>  <span class="number">0.769714</span></span><br><span class="line"><span class="number">2</span>   <span class="number">0.806476</span>  <span class="number">0.858286</span>  <span class="number">0.844000</span>  ...  <span class="number">0.837524</span>  <span class="number">0.842095</span>  <span class="number">0.861905</span></span><br><span class="line"><span class="number">3</span>   <span class="number">0.818205</span>  <span class="number">0.846154</span>  <span class="number">0.870513</span>  ...  <span class="number">0.854103</span>  <span class="number">0.851538</span>  <span class="number">0.856410</span></span><br><span class="line"><span class="number">4</span>   <span class="number">0.495882</span>  <span class="number">0.645000</span>  <span class="number">0.575392</span>  ...  <span class="number">0.700294</span>  <span class="number">0.696961</span>  <span class="number">0.682059</span></span><br><span class="line"><span class="number">5</span>   <span class="number">0.720845</span>  <span class="number">0.784695</span>  <span class="number">0.757934</span>  ...  <span class="number">0.818592</span>  <span class="number">0.814554</span>  <span class="number">0.764225</span></span><br><span class="line"><span class="number">6</span>   <span class="number">0.521991</span>  <span class="number">0.613175</span>  <span class="number">0.576967</span>  ...  <span class="number">0.637820</span>  <span class="number">0.638768</span>  <span class="number">0.635735</span></span><br><span class="line"><span class="number">7</span>   <span class="number">0.676488</span>  <span class="number">0.782634</span>  <span class="number">0.677366</span>  ...  <span class="number">0.799707</span>  <span class="number">0.803220</span>  <span class="number">0.807902</span></span><br><span class="line"><span class="number">8</span>   <span class="number">0.782540</span>  <span class="number">0.819153</span>  <span class="number">0.817460</span>  ...  <span class="number">0.810794</span>  <span class="number">0.820106</span>  <span class="number">0.832698</span></span><br><span class="line"><span class="number">9</span>   <span class="number">0.906377</span>  <span class="number">0.917005</span>  <span class="number">0.916908</span>  ...  <span class="number">0.920000</span>  <span class="number">0.916908</span>  <span class="number">0.920483</span></span><br><span class="line"><span class="number">10</span>  <span class="number">0.484762</span>  <span class="number">0.586667</span>  <span class="number">0.511688</span>  ...  <span class="number">0.642511</span>  <span class="number">0.638182</span>  <span class="number">0.614978</span></span><br><span class="line"><span class="number">11</span>  <span class="number">0.683378</span>  <span class="number">0.739111</span>  <span class="number">0.730667</span>  ...  <span class="number">0.785156</span>  <span class="number">0.779111</span>  <span class="number">0.758222</span></span><br><span class="line"><span class="number">12</span>  <span class="number">0.637956</span>  <span class="number">0.688356</span>  <span class="number">0.688267</span>  ...  <span class="number">0.670578</span>  <span class="number">0.679467</span>  <span class="number">0.688533</span></span><br><span class="line"><span class="number">13</span>  <span class="number">0.528741</span>  <span class="number">0.705778</span>  <span class="number">0.642519</span>  ...  <span class="number">0.723852</span>  <span class="number">0.733333</span>  <span class="number">0.690519</span></span><br><span class="line"><span class="number">14</span>  <span class="number">0.736569</span>  <span class="number">0.810686</span>  <span class="number">0.774216</span>  ...  <span class="number">0.818431</span>  <span class="number">0.817647</span>  <span class="number">0.819118</span></span><br><span class="line"><span class="number">15</span>  <span class="number">0.490704</span>  <span class="number">0.595869</span>  <span class="number">0.573052</span>  ...  <span class="number">0.724413</span>  <span class="number">0.706291</span>  <span class="number">0.639531</span></span><br><span class="line"><span class="number">16</span>  <span class="number">0.666977</span>  <span class="number">0.746202</span>  <span class="number">0.727907</span>  ...  <span class="number">0.754884</span>  <span class="number">0.753488</span>  <span class="number">0.760310</span></span><br><span class="line"><span class="number">17</span>  <span class="number">0.595814</span>  <span class="number">0.668372</span>  <span class="number">0.650233</span>  ...  <span class="number">0.676279</span>  <span class="number">0.657209</span>  <span class="number">0.696279</span></span><br></pre></td></tr></table></figure>

<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br><span class="line">45</span><br><span class="line">46</span><br><span class="line">47</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># -*- coding: utf-8 -*-</span></span><br><span class="line"><span class="comment"># @Time     : 2020/6/24</span></span><br><span class="line"><span class="comment"># @Author   : esy</span></span><br><span class="line"></span><br><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> matplotlib.pyplot <span class="keyword">as</span> plt</span><br><span class="line"><span class="keyword">import</span> warnings</span><br><span class="line"></span><br><span class="line"><span class="comment"># 忽略警告</span></span><br><span class="line">warnings.filterwarnings(<span class="string">&quot;ignore&quot;</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 最高准确率时对应特征数，对应的分类准确率</span></span><br><span class="line">feature = pd.read_excel(<span class="string">&#x27;E:/feature section&#x27;</span> + <span class="string">&#x27;/feature_section_class&#x27;</span> + <span class="string">&#x27;%s&#x27;</span> % <span class="number">1</span> + <span class="string">&#x27;.xlsx&#x27;</span>)</span><br><span class="line">data = pd.get_dummies(feature.iloc[<span class="number">0</span>:<span class="built_in">len</span>(feature), <span class="number">1</span>:])</span><br><span class="line">data.rename(columns=&#123;<span class="number">0</span>: <span class="string">&#x27;SGD&#x27;</span>, <span class="number">1</span>: <span class="string">&#x27;SVM&#x27;</span>, <span class="number">2</span>: <span class="string">&#x27;LSVM&#x27;</span>, <span class="number">3</span>: <span class="string">&#x27;LR&#x27;</span>, <span class="number">4</span>: <span class="string">&#x27;KNN&#x27;</span>, <span class="number">5</span>: <span class="string">&#x27;DT&#x27;</span>, <span class="number">6</span>: <span class="string">&#x27;RF&#x27;</span>, <span class="number">7</span>: <span class="string">&#x27;GBT&#x27;</span>, <span class="number">8</span>: <span class="string">&#x27;NN&#x27;</span>&#125;, inplace=<span class="literal">True</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 经过1%牺牲后的分类准确率</span></span><br><span class="line">section = pd.read_excel(<span class="string">&#x27;E:/feature section&#x27;</span> + <span class="string">&#x27;/features_section_class&#x27;</span> + <span class="string">&#x27;%s&#x27;</span> % <span class="number">1</span> + <span class="string">&#x27;.xlsx&#x27;</span>)</span><br><span class="line">df = pd.get_dummies(section.iloc[<span class="number">0</span>:<span class="built_in">len</span>(section), <span class="number">1</span>:])</span><br><span class="line">df.rename(columns=&#123;<span class="number">0</span>: <span class="string">&#x27;SGD&#x27;</span>, <span class="number">1</span>: <span class="string">&#x27;SVM&#x27;</span>, <span class="number">2</span>: <span class="string">&#x27;LSVM&#x27;</span>, <span class="number">3</span>: <span class="string">&#x27;LR&#x27;</span>, <span class="number">4</span>: <span class="string">&#x27;KNN&#x27;</span>, <span class="number">5</span>: <span class="string">&#x27;DT&#x27;</span>, <span class="number">6</span>: <span class="string">&#x27;RF&#x27;</span>, <span class="number">7</span>: <span class="string">&#x27;GBT&#x27;</span>, <span class="number">8</span>: <span class="string">&#x27;NN&#x27;</span>&#125;, inplace=<span class="literal">True</span>)</span><br><span class="line"></span><br><span class="line">x = np.arange(<span class="number">9</span>)</span><br><span class="line">y1 = [(np.array([np.array(data).tolist()[i][j] * <span class="number">100</span> <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">18</span>)])).mean() <span class="keyword">for</span> j <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">9</span>)]</span><br><span class="line">y2 = [(np.array([np.array(df).tolist()[i][j] * <span class="number">100</span> <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">18</span>)])).mean() <span class="keyword">for</span> j <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">9</span>)]</span><br><span class="line"></span><br><span class="line">width = <span class="number">0.4</span></span><br><span class="line">plt.rcParams[<span class="string">&#x27;font.sans-serif&#x27;</span>] = [<span class="string">&#x27;Microsoft YaHei&#x27;</span>]</span><br><span class="line">plt.rcParams[<span class="string">&#x27;axes.unicode_minus&#x27;</span>] = <span class="literal">False</span></span><br><span class="line">fig, ax = plt.subplots(figsize=(<span class="number">7</span>, <span class="number">5</span>))</span><br><span class="line">rects1 = ax.bar(x - width/<span class="number">2</span>, y1, width, color=<span class="string">&#x27;SkyBlue&#x27;</span>, label=<span class="string">&#x27;Before dimensionality reduction&#x27;</span>)</span><br><span class="line">rects2 = ax.bar(x + width/<span class="number">2</span>, y2, width, color=<span class="string">&#x27;IndianRed&#x27;</span>, label=<span class="string">&#x27;After dimensionality reduction&#x27;</span>)</span><br><span class="line"></span><br><span class="line">plt.xticks(x, (df.keys()), fontsize=<span class="number">10</span>, rotation=<span class="number">0</span>)</span><br><span class="line"></span><br><span class="line">plt.ylabel(<span class="string">&#x27;Average Accuracy/%&#x27;</span>, fontsize=<span class="number">15</span>)</span><br><span class="line">ax.set_title(<span class="string">&#x27;DLRW主题下降维前后准确率对比&#x27;</span>, fontsize=<span class="number">15</span>)</span><br><span class="line">ax.legend()</span><br><span class="line">plt.ylim((<span class="number">60</span>, <span class="number">95</span>))</span><br><span class="line">new_ticks = np.linspace(<span class="number">60</span>, <span class="number">95</span>, <span class="number">5</span>)</span><br><span class="line">plt.yticks(new_ticks, fontsize=<span class="number">10</span>)</span><br><span class="line"><span class="keyword">for</span> y <span class="keyword">in</span> rects1+rects2:</span><br><span class="line">    h = y.get_height()</span><br><span class="line">    ax.text(y.get_x()+y.get_width()/<span class="number">2</span>, h, <span class="string">&#x27;%.1f&#x27;</span> % h, ha=<span class="string">&#x27;center&#x27;</span>, va=<span class="string">&#x27;bottom&#x27;</span>, fontsize=<span class="number">10</span>)</span><br><span class="line">plt.show()</span><br><span class="line">fig.savefig(<span class="string">&#x27;feature_class4_number.png&#x27;</span>, dpi=<span class="number">1600</span>, bbox_inches=<span class="string">&#x27;tight&#x27;</span>)</span><br><span class="line"></span><br></pre></td></tr></table></figure>

<p>感觉效果并不好，考虑下一个步骤</p>
<p><img src="/images/feature_class3_number.png" alt="feature_class3_number"></p>
<p><img src="http://b-ssl.duitang.com/uploads/item/201610/09/20161009160331_YNsHu.jpeg"></p>

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